Determinants of Left Ventricular Characteristics Assessed by Cardiac Magnetic Resonance Imaging and Cardiovascular Biomarkers Related to Kidney Transplantation
Notice bibliographique
Résumé
Background: Cardiac magnetic resonance (CMR) imaging accurately and precisely measures left ventricular (LV) mass and function. Identifying mechanisms by which LV mass change and functional improvement occur in some end-stage kidney disease (ESKD) patients may help to appropriately target kidney transplant (KT) recipients for further investigation and intervention. The concentration of serum adiponectin, a cardiovascular biomarker, increases in cardiac failure, its production being enhanced by B-type natriuretic peptide (BNP), and both serum adiponectin and BNP concentrations decline posttransplantation. Objective: We tested the hypothesis that kidney transplantation alters LV characteristics that relate to serum adiponectin concentrations. Design: Prospective and observational cohort study. Setting: The study was performed at 3 adult kidney transplant and dialysis centers in Ontario, Canada. Patients: A total of 82 KT candidate subjects were recruited (39 to the KT group and 43 to the dialysis group). Predialysis patients were excluded. Measurements: Subjects underwent CMR with a 1.5-tesla whole-body magnetic resonance scanner using a phased-array cardiac coil and retrospective vectorographic gating. LV mass, LV ejection fraction (LVEF), LV end-systolic volume (LVESV), and LV end-diastolic volume (LVEDV) were measured by CMR pre-KT and again 12 months post-KT (N = 39), or 12 months later if still receiving dialysis (N = 43). LV mass, LVESV, and LVEDV were indexed for height (m 2.7 ) to calculate left ventricular mass index (LVMI), left ventricular end-systolic volume index (LVESVI), and left ventricular end-diastolic volume index (LVEDVI), respectively. Serum total adiponectin and N-terminal proBNP (NT-proBNP) concentrations were measured at baseline, 3 months, and 12 months. Methods: We performed a prospective 1:1 observational study comparing KT candidates with ESKD either receiving a living donor organ (KT group) or waiting for a deceased donor organ (dialysis group). Results: Left ventricular mass index change was −1.98 ± 5.5 and −0.36 ± 5.7 g/m 2.7 for KT versus dialysis subjects ( P = .44). Left ventricular mass change was associated with systolic blood pressure (SBP) ( P = .0008) and average LV mass ( P = .0001). Left ventricular ejection fraction did not improve (2.9 ± 6.6 vs 0.7 ± 4.9 %, P = .09), while LVESVI and LVEDVI decreased more post-KT than with continued dialysis (−3.36 ± 5.6 vs −0.22 ± 4.4 mL/m 2.7 , P < .01 and −4.9 ± 8.5 vs −0.3 ± 9.2 mL/m 2.7 , P = .02). Both adiponectin (−7.1 ± 11.3 vs −0.11 ± 7.9 µg/mL, P < .0001) and NT-proBNP (−3811 ± 8130 vs 1665 ± 20013 pg/mL, P < .0001) declined post-KT. Post-KT adiponectin correlated with NT-proBNP ( P = .001), but not estimated glomerular filtration rate (eGFR) ( P = .13). Change in adiponectin did not correlate with change in LVEF in the KT group (Spearman ρ = 0.16, P = .31) or dialysis group (Spearman ρ = 0.19, P = .21). Limitations: Few biomarkers of cardiac function were measured to fully contextualize their role during changing kidney function. Limited intrapatient biomarker sampling and CMR measurements precluded constructing dose-response curves of biomarkers to LV mass and function. The CMR timing in relation to dialysis was not standardized. Conclusions: The LVESVI and LVEDVI but not LVMI or LVEF improve post-KT. LVMI and LVEF change is independent of renal function and adiponectin. As adiponectin correlates with NT-proBNP post-KT, improved renal function through KT restores the normal heart-endocrine axis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».